Discovering synergies between Design and Financial Literacy
Bibliographic record
Abstract
Money. Key to most of our everyday requirements and desires. Something which drives our basic needs. Seeming almost innate in its very existence and yet superficial in its actual value, one cannot escape its existence. Despite all its forms, representations, utilities, and volatilities, money and finance are undeniably a key aspect to our lives in the 21st century. \n \nSo, what is money? Is it a piece of paper? Or a piece of metal? Does its physicality even matter? Isn’t it amusing to think that such core components of our lives almost go unnoticed, or are ideally ‘delegated’ to someone who knows it better? Thus, is there a gap that needs to be filled? Are we financially literate? \n \nThis paper looks to explore the different manifestations of the Canadian financial system, from the lens of the participants who might be the most vulnerable. Using inclusive design methodologies, the idea is to identify the range of pain-points associated with finance from co-designers who have first hand experience. These co-designers were mostly young newcomers to Canada who are trying to find their way within a new ecosystem, without any formal financial education. \n \nThe paper also aims to examine the vehicles that enable financial literacy and tries to understand the gaps associated with them. The main purpose is to find ways in which visual design can further solidify or simplify these concepts for the people in need. These visual design outputs are expressed in the form of infographics with minimal supporting text, aimed towards mitigating the implications of terminologies and jargon. \n \nThe ultimate goal is towards identifying and prototyping solutions for the diverse range of needs among the participants, rather than arriving at a statistical majority. This looks to tie into the Government of Canada’s National Financial Literacy Strategy 2021-2026, which aims towards enabling a financially resilient future. \n \nKeywords: finance, money, literacy, Canada.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".